Abstract
Biomedical named entity recognition (BNER) is an important task in biomedical natural language processing, in which neologisms (new terms, words) are coined constantly. Most of the existing work can only identify biomedical named entities with flattened structures and ignore nested biomedical named entities and discontinuous biomedical named entities. Because biomedical domains often use nested structures to represent semantic information of named entities, existing methods fail to utilize abundant information when processing biomedical texts. This paper focuses on identifying nested biomedical named entities using a boundary assembly (BA) model, which is a cascading framework consisting of three steps. First, start and end named entity boundaries are identified and then assembled into named entity candidates. Finally, a classifier is implemented for filtering false named entities. Our approach is effective in handling nesting and discontinuous problems in biomedical named entity recognition tasks. It improves the performance considerably, achieving an F1-score of 81.34% on the GENIA dataset.
Highlights
With rapid progress in biomedical research, the biomedical literature is vast
MATERIALS This paper focuses on recognizing nested named entity (NE) from biomedical texts
In this paper, we use the boundary assembly (BA) method to implement the biomedical NE recognition task. It is a cascaded framework focusing on nested NE recognition, which consists of three steps: boundary detecting, boundary assembling and entity discriminating
Summary
YANPING CHEN1, YING HU1, YIJING LI., RUIZHANG HUANG, YONGBIN QIN, YUEFEI WU, QINGHUA ZHEN, PING CHEN3, 1College of Computer Science and Technology, Guizhou University, Guizhou 550025, China 2Department of Computer Science and Technology, Xi’an Jiaotong University, Xi’an 710049, China 3Department of Computer Science, University of Massachusetts Boston, Boston, MA 02125, USA
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have